Autonomous Emergency Landing of a Helicopter

Overview

In this project, we design a system that safely lands a helicopter after its engines have failed. The system plans alternate routes to multiple landing zones thus ensuring robustness against unmodelled elements.

Abstract

Helicopters are in use for a variety of missions such as continuous surveillance, delivering goods in constrained environments and emergency rescue operations. System failures during a mission can be catastrophic unless the pilot performs quick and coordinated actions to initiate autorotation, finds a good landing zone and safely lands. In such situations, an autonomous landing system could react quicker to regain control, however current autonomous autorotation methods only generate dynamically feasible trajectories without considering important terrain and time constraints.
In this paper, we address the problem of autonomously landing a helicopter while considering a realistic context: multiple potential landing zones, geographical terrain, sensor limitations and pilot contextual knowledge. The heart of the solution is based on planning alternate routes in real time, which we show effectively handles all of the above factors and allows the pilot to choose a flight path. Our results show that after 4500 trial runs, our planning algorithm, RRT*-AR, outperforms the state of the art RRT* by providing the human 280% more alternate routes 67% faster on average. To the authors' knowledge, this is the first study that addresses both issues of dealing with real time constraints as well as decreasing the possibilities of failure for autorotation landing in a realistic scenario.

Details

A demonstration of the planning system is shown in the following video.

The planning system was tested with data from real flights. An example is shown below .

Figure 1. Sample route set for safe landing after engine failure. This example shows a set of potential engine-out trajectories for a Bell 206 using the proposed RRT*-AR algorithm. The paths were calculated from a registered dataset from prior elevation maps, pointcloud, and image data collected onboard the aircraft.

Links

Here is an example of a pilot executing a rather extreme maneuver in real life. The goal of our project would be to have our system push the performance limit beyond what a human pilot can achieve in a more dangerous environment .